Title :
Applications of simulated annealing minimization technique to unsupervised classification of remotely sensed data
Author :
Yuan, Hui ; Khorram, Siamak ; Dai, X. Long
Author_Institution :
Center for Earth Obs., North Carolina State Univ., Raleigh, NC, USA
Abstract :
The research in this paper is designed to develop algorithm and applications to apply the simulated annealing to unsupervised classification and compare the classification results with that of the K-means. It is known that the K-means can only produce local minimal solutions. We propose to use the simulated annealing to solve this problem and find a global solution in the clustering process. Details about how to choose parameters and cooling schedule are discussed based on experiment results. Finally, the future work to extend this research is proposed and presented
Keywords :
geophysical signal processing; image classification; minimisation; simulated annealing; terrain mapping; unsupervised learning; K-means; clustering process; remotely sensed data; simulated annealing minimization technique; unsupervised classification; Algorithm design and analysis; Clustering algorithms; Computational modeling; Cooling; Earth; Euclidean distance; Minimization methods; Partitioning algorithms; Remote sensing; Simulated annealing;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International
Conference_Location :
Hamburg
Print_ISBN :
0-7803-5207-6
DOI :
10.1109/IGARSS.1999.773425
